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Project topic: Adaptive cloud based services for mobile users Paper to present: Competitive Analysis for Service Migra

Project topic: Adaptive cloud based services for mobile users Paper to present: Competitive Analysis for Service Migration in VNets. Zahra Abbasi. Introduction to the project. Assumptions: Providing service for mobile users through clouds Cloud based services:

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Project topic: Adaptive cloud based services for mobile users Paper to present: Competitive Analysis for Service Migra

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  1. Project topic: Adaptive cloud based services for mobile usersPaper to present: Competitive Analysis for Service Migration in VNets Zahra Abbasi

  2. Introduction to the project • Assumptions: • Providing service for mobile users through clouds • Cloud based services: • Couple of DCs that are networked • Infrastructure of the network and DC are hidden from service provider and users • Service can be hosted in any DC of the cloud • The access point of mobile users changes over time • Adaptive cloud based services • Dynamically changing the number and the locations of virtual servers to: • Minimizing energy consumption • Maximizing quality of service for mobile users • Modeling the problem as an optimization problem • Simulation based evaluation

  3. Competitive Analysis for Service Migration in VNets MarcinBienkowskiAnjaFeldmann Dan Jurca Wolfgang KellererGregorSchaffrath Stefan Schmid and JoergWidmer University of Wrocław, Poland, docomolab-euro.com And T-Labs / TU Berlin Berlin, Germany ACM SIGCOMM 2010

  4. Introduction-Motivation • Virtualized network/Cloud computing • The detail of infrastructure is hidden for service providers and users • Applications can be hosted in any node in a dynamic fashion

  5. Introduction-Motivation • Virtualized network/Cloud computing • The detail of infrastructure is hidden for service providers and users • Applications can be hosted in any node in a dynamic fashion • Potential advantageous for mobile users • Improving the quality of service by dynamic service migration • Service migration management • Migration cost: Service outage, migration cost • Service cost: Delay of requests • Research question: • To migrate or not to migrate? (online) • How to compare with the offline optimal solution?

  6. Overview on contributions and results • Proposing an online service migration for mobile based services • Competitive analysis and deriving the competitive ratio (log n) • Online vs. offline • Offline: All access point information of users is known in advance • Online: The past and current information is available

  7. System model and assumptions • Virtualized network • G=(V,E) • A bandwidth is associated with any edge of the set E • Service can be hosted in any node of the set V • Access cost of users • Number of hops in the shortest path from the access point to the server • Migration cost Costacc(A)=2 A Bw=10 Bw=10 Costmig(S2,S3,size(s)=100)=10

  8. System model and assumptions • The system makes decision for the migration on time slots called rounds • Requests access points changes at rounds • Access points: t0={A,B}, t1={C,D} where {A,B,C,D} are nodes of the set V. • There is only one service

  9. Online algorithmStrike balance between Costacc and Costmig • Given • an initial physical location of the server V0∈ V • An initial access point set of requests ⊆ V • Phase: multiple of rounds • Step 1: • Migrate to v’ if Lv >= β, where v’ is randomly chosen among nodes whose Lv’ >= β • Reset Lv if all Lv >= β • End of phase

  10. Online Alg. Example Phase 7(A) 7+10+4=21(C) 21+2=23(C) 23(C)

  11. Optimal versus online-example • BW: 10, Latency: 11, size(s)=100 t0: {C}, v0=B t1:{B} Total cost for online alg: 20 Total cost for optimal: 11 B C A

  12. Competitive analysis • For a given phase: Cost(OPT) >= β • Case1:If the optimal solution does a migration then: • Cost(OPT) >= β • Case 2: If it does not migration • OPT pays L(v) for a fixed v during a phase , where L(v) >= β • Expected number of migration for the ALG is at most Hn (nth harmonic number, O(Hn)=logn) • {V}: the descending ordered set of vertices whose L(v) reaches β • Prev Ex: v1=A, v2=B, v3=E, v4=C, v5=D • Ti: expected # of migration given Vi as the initial point: • Recursive relationship for any vi and vj j>=i: Ti=1+Tj where j>i

  13. ConclusionExtending the modeling to our problem • Simplification of assumption to derive the competitive ratio • Extension of the model for the cloud based mobile services • Energy cost of centers are taken into account • Servers are allowed to be duplicated

  14. The Case for VM-based Cloudlets in Mobile Computing MahadevSatyanarayanan, ParamvirBahl Ramon Caceres, Nigel Davies

  15. Introduction • Cloud computing is a solution for resource-poverty of mobiles. • Architecture for Virtual Machine Provisioning • Customized Service nearby • Cloud Computing Limitations • Cloudlet Approach • Proof-Of-Concept • Challenges

  16. Cloud Service for Mobiles • Battery, Weight, and Size are the most priority of mobile manufacturers • Virtualization to face resource poverty is needed • Human cognitive applications • Facial/speech recognition • Scene interpretation • Voice synthesis/translation • Computational intensive applications • Internet Delay and Jitter are harmful for interactive/real-time applications

  17. Cloud Computing Limitations

  18. Future of Cloud Computing for Mobiles • Using Wireless LAN • More Bandwidth • Less Delay/Latency, Less Jitter • No rely on distant cloud • Local Data Centers • Cloudlets • Wireless access point • PC / Computer Cluster • Internet Access

  19. Cloudlet: Tiny cloud nearby • On-hop wireless LAN, morebandwidth • Provide real-time response time , low delay • Consume less energy, more green • Wide spread, decentralized, more ubiquitous • Self-managed, easily setup, more chip • Can work connection less, independent from Internet Data Center in a Box

  20. Cloudlet State Diagram

  21. Cloudlet customization • Customized VM transiently by mobiles • Mobile Clients: • Pre-use customization • Use service • Post-use cleanup • Dynamic Virtual Machine Synthesis • VM base in cloudlet • VM overlay as service application • Lunch VM on cloudlet

  22. Dynamic VM Synthesis State Diagram Base VM Install the Overlay VM Lunch VM Overlay VM VM Residue Done

  23. Proof-of-Concept (Kimberly)

  24. KCM VM Synthesis Delay

  25. Cloudlet Challenges • Initiation Delay • Business Model • Size • Trust & Security • Migration & Handout

  26. Conclusion • New approach of Cloud Computing for Mobiles • Nearby resource-rich computers • High Bandwidth and Low Latency • Good for Local Applications • Investment & Infrastructure • Business & Marketing

  27. Thank YOU

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